Statistical Multi-Faults Localization Strategy of Switch Open-Circuit Fault for Modular Multilevel Converters Using Grubbs Criterion

Abstract

Fault localization is one of the most important issues for the MMC consisting of large number of switches. This paper proposes a statistical multi-faults localization strategy for the MMC, where a feature extraction algorithm of capacitor voltage variations is proposed to extract the features of the MMC based on the exponential smoothing relationship among the capacitor voltages. Based on the extracted features, faults in the MMC can be easily located with the Grubbs Criterion according to the Grubbs Criterion Table. The proposed Grubbs Criterion-based fault localization strategy can construct concise simple features samples for the MMC containing all exponential smoothing global capacitor voltage variables, and accordingly it can locate faults with short time for the MMC. In addition, it not only does not require a large number of training data samples, but also does not require the creation of complex mathematical models and manual setting of empirical thresholds. It is compatible with both single and multiple switches open-circuit failures. The results of the simulation confirm the effectiveness of the proposed strategy.